Systems and methods for retrieving information stored in a matrix database or structured database for extracting accommodations between entities on the basis of similar properties between the entities is known. The term matrix database in the content here means that each entity has one or more properties that are organized in categories. For example, in the category of “music band” stored the music band that each entity likes and in the category of “books” stored the name of the book, author of the book etc that each entity liked or read. These systems are based on data that will be fed into the system database in a categorical manner such that a user can find entities with a certain property by knowing the properties of each entity in the database.
Predetermined terms used in the following description are provided to help understanding the present invention and the use of the predetermined terms may be modified into different forms without departing from the spirit of the present invention.
The term structured database refers to data that resides in a fixed field within a record or file. This includes data contained in relational databases and spreadsheets. Structured data depends on creating a data model—a model of the types of data that will be recorded and how they will be stored, processed and accessed. This includes defining what fields of data will be stored and how that data will be stored: data type (numeric, currency, alphabetic, name, date, address) and any restrictions on the data input (number of characters; restricted to certain terms such as Mr., Ms. or Dr.; M or F).
The term unstructured data refers to those things that can't be so readily classified and fit into a neat box: photos and graphic images, videos, streaming instrument data, webpages, pdf files, PowerPoint presentations, emails, blog entries, wikis , word processing documents etc.
The term semi-structured data refers to a cross between the two. It is a type of structured data, but lacks the strict data model structure. With semi-structured data, tags or other types of markers are used to identify certain elements within the data, but the data doesn't have a rigid structure. For example, Emails have the sender, recipient, date, time and other fixed fields added to the unstructured data of the email message content and any attachments. Extensible Markup Language (XML) and other markup languages are often used to manage semi-structured data.
The term semantic web refers to web pages contain enough self-describing data that machines will be able to navigate them as easily as humans do. This let computers better assist us in answering questions and managing our ever more complicated world. Some of the semantic web technologies are the resource description network (RDF), web ontology language (OWL), semantic web rule language (SWRL), SPARQL Protocol and RDF query language (SPARQL), Semantic application platforms, and statement-based datastores such as triplestores, tuplestores and associative databases.
The concept of the social semantic web subsumes developments in which social interactions on the Web lead to the creation of explicit and semantically rich knowledge representations. The Social Semantic Web can be seen as a Web of collective knowledge systems, which are able to provide useful information based on human contributions and which get better as more people participate. The Social Semantic Web combines technologies, strategies and methodologies from the Semantic Web, social software , the Web 2.0 and Web 3.0.
The term RDF is a family of World Wide Web Consortium (W3C) specifications originally designed as a metadata data model. It has come to be used as a general method for conceptual description or modeling of information that is implemented in web resources, using a variety of syntax notations and data serialization formats.
The term Web 2.0 in the content of the present application refers to several major themes, including Asynchronous Java script and XML (AJAX), social networking, folksonomies (also known as collaborative tagging, social classification, social indexing and social tagging), lightweight collaboration, social bookmarking, and media sharing.
The term web 3.0 in the content of the present application refers to an Internet-based services that collectively include semantic web, microformats, natural language search, data-mining, machine learning, recommendation agents, and artificial intelligence technologies which emphasize machine-facilitated understanding of information in order to provide a more productive and intuitive user experience. Web 3.0 is an environment consisting of intelligent web-based semantic applications, where the web is a database of information published via reusable formats such as XML, RDF and other micro formats. Web 3.0 may bring the realization of the semantic web, where meaning can be extracted from data representations such as hypertext and utility driven by meaning.
U.S. Pat. No. 8,386,499 discloses systems and methods for modeling relationships between entities on a network using data collected from a plurality of communication channels including social data, spatial data, temporal data and logical data within a W4 Network. The W4 Network personalizes and automates sorting, filtering and processing of W4COMN communications delivered or requested to be delivered using personalized value-based ranking and encoding of data, which is modeled from the point-of--view (POV) of any specific user, topic or node in the W4 Distributed graph. The W4COMN is a collection of users, devices and processes that foster both synchronous and asynchronous communications between users and their proxies. POV modeling supplies comparative value services to users which entail individuated data models to be aggregated and used in customization and personalization forecasting for each user and their associated data management needs.
One object of the present invention is to understand the position(s) or opinion(s) of a predetermined public of entities regarding to a specific issue, in particularly understanding who are the influencers entities who are the influenced entities and who are the entities in the public that have the potential to become influenced by others regarding to the specific issue.
Yet another object of the present invention is to reduce the ability of the influencer's entities to influence the influenced and potential influenced entities.
Yet another object of the present invention is to reduce the number of influenced entities regarding to a specific issue and to reduce the spreading of the influence by the influenced entities on the public that is not part in the influenced entities circle.
Yet another object of the present invention is to identify the influencer, the influenced and the potential influence entities and to test and execute actions in the network particularly but not limited to the social network for reducing or increasing the relevant of the particular issue, to reduce or to increase the spreading intensity of the particular issue among the public. In addition another object of the present invention is to measure and estimate the success of the actions taken regarding to the particular issue and the entities.
Yet another object of the present invention is to automatically collect and analyze data of public in a small and large scale for example up to millions of entities.